Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu xi
... Transformation properties of layered machines , 103 6.6 A sufficient condition for image - space linear separability , 107 6.7 Derivation of a discriminant function for a layered machine , 109 6.8 Bibliographical and historical remarks ...
... Transformation properties of layered machines , 103 6.6 A sufficient condition for image - space linear separability , 107 6.7 Derivation of a discriminant function for a layered machine , 109 6.8 Bibliographical and historical remarks ...
Sivu 103
... Transformation properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
... Transformation properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
Sivu 107
... transformation such that g1 ( 1 ) and ( 2 ) are placed on opposite sides of the fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 92 ( 1 ) ...
... transformation such that g1 ( 1 ) and ( 2 ) are placed on opposite sides of the fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 92 ( 1 ) ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |